In the article "Rates and mechanisms of resistance development in Mycobacterium tuberculosis to a novel diarylquinoline ATP synthase inhibitor" Huitric et al. looked at almost 100 different bacterial mutants that the authors identified from clinical isolates. They tried to establish how quickly M. tuberculosis can develop mutations in vitro that will confer resistance to the new investigational drug TMC207/R207910. Furthermore, Huitric et al. were interested in studying the mutants in more detail to learn about the mechanisms by which the bacterium can escape the bactericidal effect of TMC207/R207910.

When looking at the level of resistance, the authors determined a 4- to 128-fold increase in MIC (Minimum Inhibitory Concentration). Increasing the concentration of TMC207/R207910 resulted in a decrease of the rate at which high-level resistence occurred. How does this in vitro result translate to the clinical context? If the therapeutic dose in patients could be equal to or higher than the concentration at which such mutations do not occur while still avoiding toxicity, the positive effects of TMC207/R207910 might be even higher as clinical resistance would be less frequent.

Huitric et al. also studied the sequence of one or more of the genes that encode the bacterial ATP synthase in approximately half of the mutants. The authors identified mutations that would result in five different amino acid substitutions in the atpE gene. This gene had previously been identified and linked to the novel mechanism of action of TMC207/R207910. However, for 38 of the mutants no further mutations in the ATP synthase related genes were identified. This suggests that Mycobacterium tuberculosis has different mechanisms by which the bacterium can escape the effects of TMC207/R207910.

A review of the mechanism of action of TMC207/R207910 can be found in this Science Enhanced Perspectives article (free access). The article by Huitric et al. has just been published in Antimicrobial Agents and Chemotherapy. Please have a look here for further information.

All next-gen sequencing platforms (actually the 2nd generation systems) cannot directly sequence RNA (also referred to as "RNA seq"). They need to first convert the RNA into cDNA which in turn can be sequenced. While the different sequencing technologies themselves are very accurate and reproducible in generating the sequence reads, the enzymatic conversion step during the generation of the sequencing library can introduce errors or sequence biases.

On the other hand, third generation sequencing technologies (e.g., Helicos BioSciences or Pacific Biosiences) offer the potential to circumvent this step as they sequence the molecule directly. However, only one platform is already commercially available and it will be a challenge to adjust all parameters (e.g., buffers, reagents, RNase-free environment) for each platform to ensure that the sequence data will be of high quality and of sufficient read length.

Even though the providers of next-gen sequencing and a number of scientists push the public perception of using next-gen sequencing instead of microarrays for transcriptome analysis (see for example the Wikipedia article on RNA seq), routine transcriptome analysis is still predominantly carried out via microarray technology. However, focusing on enabling reproducible high quality RNA sequencing on a third generation platform early on could result in a significant market opportunity and a clear competitive advantage over microarrays.

Over the last couple of weeks I have had the opportunity to participate in the NGI Venture Challenge which is organized by the Netherlands Genomics Initiative (NGI). The aim of this initiative is to educate people with an idea of commercial potential in the life sciences on how to write a venture plan and "turn a promising idea into a sound business case".

Attending two 3-day workshops and a number of individual coaching sessions we learned a lot about looking at our scientific findings in the field of human lung regeneration from a business point of view. Today we had to give our pitch in front of a jury in Utrecht. Even though we did not win the price of 25,000 Euro the new insights we gained and the new contacts we made are invaluable.

Thanks very much to the coaches and the Netherlands Genomics Initiative!

I have written about this before: research depends increasingly on IT. Both the IT infrastructure as well as appropriate software (predominantly written by rapid prototyping for cutting edge science) need to be provided in a proactive way to anticipate the trends in current molecular biology (e.g., microarrays, next-gen sequencing, high content screening). The question is how to approach IT support for research optimally?

Here are some elements I consider to be crucial:

Physical proximity of a key IT support person to the scientist for less demanding projects and an integrated IT team member for IT-heavy projects such as next-gen sequencing

Single point of contact function of the IT person towards the scientist while delegating specialized tasks to IT experts

Research mind set of the key IT support people asks for a research approach towards IT whereby novel ways of providing IT solutions are assessed and expertise is build up over time about the kind of scientific questions that are addressed by a given team (e.g., detection of differential gene expression in microarray experiments from different species and studies)

The IT support for research should be considered as an investment and not as a cost center (like e.g., heating, cleaning)

Concepts and approaches towards general IT (such as standardization of operating systems, hardware, applications) do not work for a research environment where novel approaches are required not only in the lab but also in the way we analyze and interpret our experimental data